Application of aerosol-ice nucleating particle closure to establish the leading parameters governing ice crystal number concentration under commonly observed mixed-phase cloud conditions

In the atmosphere, cloud and precipitation particles may be liquid- or ice-phase or a combination of both. While in recent years there has been tremendous progress in understanding how liquid-phase droplets are first formed (or nucleated), the nucleation of ice crystals has remained elusive. Where the air is too warm for droplets to spontaneously freeze (roughly –38°C), ice nucleation may be promoted by aerosol

aerosols

Small particles or droplets, such as wildfire smoke, volcanic gases, or salty sea spray, that float in the air. They are emitted by both natural events and human activities.

Source: NASA Global Climate Change Source

particles (submicrometer- and supermicrometer-sized airborne particulate matter) that can act as ice nucleating agents, but differing models for such nucleation give grossly differing results. Indeed, ice formation is considered one of the remaining grand challenges in the atmospheric sciences. Ice nucleation plays a crucial role in precipitation formation and alters the radiative properties of clouds, thereby affecting Earth’s climate system.

The objectives of this project are to gain a predictive understanding

predictive understanding

Predictive understanding refers to the ability of a system, typically an artificial intelligence or machine learning model, to anticipate future events or outcomes based on historical data and patterns.

Source: Source

of the chain that leads from aerosols to ice-nucleating particles

ice-nucleating particles

Tiny specks of material suspended in the air that act as starting points for the formation of ice crystals in clouds.

Source: PNNL Pacific Northwest National Laboratory Source

to ice crystal number concentrations in stratiform mixed-phase clouds. This project takes a holistic bottom-up approach to linking aerosol particle properties to ice crystal number concentrations via the prediction of ice-nucleating particles. This will be achieved by a two-pronged approach. In the first step, we will perform a modeling closure analysis between simultaneously measured aerosol properties and ice-nucleating particle number concentrations using data obtained from U.S. DOE Atmospheric Radiation Measurement field campaigns. Namely, we will use the measured physicochemical parameters (e.g., size and composition) of the aerosol population to predict the observed number of ice-nucleating particles using different models, and then compare predictions with observations to evaluate the degree to which agreement is achieved when accounting for experimental uncertainties. Results will provide evaluation of the skill of differing models to accurately predict ice-nucleating particle number concentrations. In the second step, we will develop and apply a simplified model of supercooled stratiform mixed-phase clouds themselves, which will be constrained by long-term U.S. DOE Atmospheric Radiation Measurement observations and large eddy simulations. This simplified cloud model will allow us to further evaluate our predictive capability of ice crystal number concentrations from aerosol characteristics using differing ice nucleation models. The simplified cloud model will include a state-of-the-art particle-resolved aerosol model that allows to simulate the aerosol particle physicochemical properties in detail, needed to predict ice-nucleating particle number concentrations. The applied aerosol population will be driven by climate-model derived aerosol fields. This approach will assess our ability to achieve gross climatological agreement between simulated and observed aerosol properties and ice crystal number concentrations.

Tasks can be broken down as follows:

1. We will advance our understanding of the aerosol physicochemical parameters that yield prediction of ice-nucleating particle number concentrations. We will employ field data of a recent U.S. DOE Atmospheric Radiation Measurement campaign that acquired aerosol properties and ice-nucleating particle number concentrations. Those data will also be placed in context with climate models typically used in the scientific community.

2. We will develop an aerosol-cloud column model. This involves long-term measurement statistics to generate realistic configurations of a simplified model for two common single-layer cloud types, suitable for efficient coupling to models of a range of different complexities that link aerosol properties to ice-nucleating particles.

3. We will examine how ice-nucleating particle number concentrations are linked to ice crystal number concentrations. We will use our model simulations to assess the parameters governing the relationship between aerosol, ice-nucleating particles, and ice crystal number concentrations with model-derived aerosol properties. Lastly, as a “reality check”, we will compare cloud parameter combinations generated by our models with long-term U.S. DOE Atmospheric Radiation Measurement observations.

This research project seeks to guide concrete improvements in the current cloud and climate modeling approaches to ice formation by advancing understanding of the coupling between detailed aerosol physicochemical properties and in-cloud ice crystal number concentrations. Results will be particularly relevant to understanding how aerosol particles modify the ice formation process in mixed-phase clouds, which are key climate players.